## Migration From Other Enterprise Search Systems

### Google Search Appliance (GSA) / Google Mini

Fess provides a [Google Search Appliance](https://enterprise.google.com/search/products/gsa.html) (GSA) compatible API. To enable this API, set `web.api.gsa=true` to system.properties. This will enable an enpoint at `<Fess Server Name>:8080/gsa`. When a search query is sent to `<Fess Server Name>:8080/gsa/?q=QUERY`, a GSA compatible response will be returned

*[Fess Site Search](https://github.com/codelibs/fess-site-search)* is a free alternative to [Google Site Search](https://enterprise.google.com/search/products/gss.html). For more details, see the [FSS JS Generator documentation](https://fss-generator.codelibs.org/docs/manual).

## Website

-e .
-r requirements-docs-tests.txt
mkdocs-material==9.4.7
mdx-include >=1.4.1,<2.0.0
mkdocs-redirects>=1.2.1,<1.3.0
typer >=0.12.0
pyyaml >=5.3.1,<7.0.0
# For Material for MkDocs, Chinese search
jieba==0.42.1
# For image processing by Material for MkDocs
pillow==10.2.0
# For image processing by Material for MkDocs
cairosvg==2.7.0
mkdocstrings[python]==0.23.0
griffe-typingdoc==0.2.2
# For griffe, it formats with black
black==24.3.0

  @JvmField val name: ByteString,
  /** Value in UTF-8 encoding. */
  @JvmField val value: ByteString,
) {
  @JvmField val hpackSize = 32 + name.size + value.size

  // TODO: search for toLowerCase and consider moving logic here.
  constructor(name: String, value: String) : this(name.encodeUtf8(), value.encodeUtf8())

  constructor(name: ByteString, value: String) : this(name, value.encodeUtf8())

 * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
 */

package org.jetbrains.kotlin.analysis.api.descriptors

import com.intellij.psi.search.GlobalSearchScope
import org.jetbrains.kotlin.analysis.api.KtAnalysisApiInternals
import org.jetbrains.kotlin.analysis.api.KtAnalysisNonPublicApi
import org.jetbrains.kotlin.analysis.api.KtAnalysisSession

### Machine Learning modelleri, spaCy ve FastAPI - Features

* **spaCy** NER model entegrasyonu.
* **Azure Cognitive Search** yerleşik istek biçimi.
* Uvicorn ve Gunicorn ile **Production ready** Python web server'ı.
* Dahili **Azure DevOps** Kubernetes (AKS) CI/CD deployment.

3. **Google employees only**: Google employees can use an internal-only tool
called "MLCI" that makes testing more convenient: it can execute any full CI job
against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

# Here is a single-line example of running a script on Linux to build the

 * Google search:
 *
 * ```java
 * HttpUrl url = new HttpUrl.Builder()
 *     .scheme("https")
 *     .host("www.google.com")
 *     .addPathSegment("search")
 *     .addQueryParameter("q", "polar bears")
 *     .build();
 * System.out.println(url);
 * ```
 *
 * which prints:
 *
 * ```
 * https://www.google.com/search?q=polar%20bears
 * ```
 *

### spaCyとFastAPIを使用した機械学習モデル - 機能

* **spaCy** のNERモデルの統合。
* **Azure Cognitive Search** のリクエストフォーマットを搭載。
* **リリース可能な** UvicornとGunicornを使用したPythonウェブサーバ。
* **Azure DevOps** のKubernetes (AKS) CI/CD デプロイを搭載。
* **多言語** プロジェクトのために、セットアップ時に言語を容易に選択可能 (spaCyに組み込まれている言語の中から)。

<a href="https://github.com/deepset-ai/haystack/" target="_blank" title="Build powerful search from composable, open source building blocks"><img src="https://fastapi.tiangolo.com/img/sponsors/haystack-fastapi.svg"></a>